Field
[0001] The present invention relates to a method for controlling power distribution from
a plurality of power sources connected to an electrical power distribution network.
Background
[0002] Figure 1 shows a portion of a typical electrical power distribution network 10. The
network comprises a number of branches connecting nodes indexed 00 to 08. A plurality
of power sources A to D are connected to nodes 03, 05, 07 and 08 respectively and
demand is drawn from the network at the nodes indicated with the arrows 12. The network
10, in this case, is fed through two transformers 14 connected in parallel. In the
example shown, the power sources are indicated as being wind turbines, although it
will be appreciated that the present invention is concerned with mitigating problems
using any form of power resource capable of reactive power control including wind,
photovoltaic, and hydro sources. Also, while the power sources are shown as individual
turbines, these can equally be wind farms including a plurality of turbines or any
such group of generators.
[0003] It will be appreciated that many such power sources are connected to remote portions
of distribution networks where the capacity of the infrastructure connecting power
sources to demand may be limited. The physical properties of the conductors and loads
within the network cause both voltage magnitude and angle to vary and so cause the
flow of reactive power. Any reactive source or sink can reduce the active power transfer
capacity of network branches and could lead to congested power flows in the network.
[0004] The power available from sources A to D can vary largely according to prevailing
environmental conditions to the extent that, under certain operating conditions, providers
can be asked by network operators to curtail active power generation to avoid network
congestion and breaching thermal constraints i.e. current limits for the network.
[0005] In short, there are two conflicting constraints in play, maintaining the terminal
voltage of a generator at an acceptable level (to the generator) to allow generated
power to be delivered and monetized while ensuring thermal constraints are not breached.
WO2015/193199 published on 23 December 2015 discloses producing and defining a relationship between local voltage and power measurements
at a node of an electrical network and system conditions on a remote branch of the
network. These local measurements are used to determine an optimal voltage set point
at the node that, if realised by a reactive power resource, would affect the flow
of reactive power or line current at one or more particular remote branches of the
power system in a manner captured by the derived relationship. The change in reactive
power required to obtain this voltage set point is also calculated based on local
measurements.
[0006] US2010/237834 discloses a method for calculating reactive power injection for a distributed generator
using local measurements at the connection point and a model of the power grid.
Summary
[0007] According to the present invention there is provided a method for controlling power
distribution according to claim 1.
[0008] This method is not limited to controlling only generators, but any devices that inject
and absorb reactive power even without producing active power.
[0009] Embodiments employ local control, by contrast with other fit-and-forget approaches
currently used in the operation of distributed generation namely; Automatic Voltage
Regulation (AVR Mode) and constant Power Factor (PF Mode).
Brief Description of the Drawings
[0010] Embodiments of the present invention will now be described, by way of example, with
reference to the accompanying drawings, in which:
Figure 1 shows an exemplary power distribution network including a number of renewable
power sources controlled according to an embodiment of the present invention;
Figure 2 illustrates generally the phases of network modelling disclosed in WO2015/193199;
Figure 3 illustrates conflicting constraints for a given generator within a network
such as shown in Figure 1;
Figure 4 illustrates the steps involved in controlling a generator as disclosed in
WO2015/193199;
Figure 5 shows the method for determining an adjusted voltage set point as disclosed
in WO2015/193199;
Figure 6 shows the combinations of calculations employed in the method of Figure 4;
Figure 7 is a table indicating various scenarios for restricting active power; and
Figure 8 illustrates a method for determining adjusted set points according to an
embodiment of the present invention.
Description of the Preferred Embodiments
[0011] Referring to Figure 2, embodiments of the present invention first of all involve
building an electrical model reflecting the electrical behaviour of a distribution
network such as the network 10, step 1.
[0012] We would first of all introduce some nomenclature used in describing this modelling:
P - Active Power
Q - Reactive Power
θ - Voltage Phasor Angle
V - Voltage Phasor Magnitude
gij - Series Conductance for branch ij
bij - Series Susceptance for branch ij
gsi - Shunt Conductance at node i
bsi - Shunt Susceptance at node i
N - Total number of nodes
|
Iij| - Current Flow in branch ij
- Active Power Voltage Sensitivity
- Reactive Power Voltage Sensitivity
- Active Power Voltage Angle Sensitivity
- Reactive Power Voltage Angle Sensitivity
[0013] Active power,
Pij, reactive power,
Qij, and line current magnitude, |
Iij|, for each branch of the network can be defined with equations such as equations
(1) to (3), although other equations could be used:
[0014] Calculating the complex power flow at any node
i of the network involves writing two functions for all nodes
N, one for active power and another for reactive power, for example, as in equations
(4) and (5):
[0015] In Step 2, a power flow analysis, for example, Newton-Raphson power flow analysis,
is undertaken to assess the impact of the generators at the various nodes of the network
over the generators' range of all possible active power and reactive power operating
points, at a given system demand. This analysis can be performed using a power system
analysis program, such as DIgSILENT PowerFactory, and/or using a dedicated solution
implemented with for example, Mathworks Matlab. Using a power system analysis program,
the active power and reactive power generation of all generators are independently
incremented between the bounds of their respective limits, capturing all combinations
of complex power injection, and the calculated voltage magnitude and angle for every
node are recorded for each combination. These node results can then be used, for example,
in a Matlab script to back calculate a Jacobian matrix including every converged power
flow.
[0016] Thus, the voltage and phase
V and
θ at every node
i of the network for every combination of active and reactive power being generated
by the network generators, in this case A to D, at a given demand being drawn from
nodes 12 is calculated. In the embodiment, this demand is a minimum system demand
at each of the nodes 12. These demand values can be set to P=Q=0 at each of the nodes
12; or they can comprise individual estimates for P and Q based on actual historical
and/or predicted values.
[0017] The power flow analysis captures the independent variables associated with changes
in active and reactive generator power injections affecting the voltage angle and
magnitude in a Jacobian matrix. The Jacobian matrix is formed by taking the coefficients
of voltage angle and magnitude and writing equations (4) and (5) in matrix notation
as follows:
[0018] This Jacobian matrix encapsulates the properties of the power system and reflects
the changes in voltage angle
θ and magnitude
V that occur at a given network node
i due to the injection of active and reactive power at any network node to which a
generator is connected.
[0019] The information captured in the Jacobian matrix are the node sensitivities of a converged
power flow solution, although some of these may be null, reflecting, for example,
where a generator might not have any effect on a remote branch of the network.
[0020] In Step 3 of FIG. 2, a nonlinear regression technique is applied to the results from
step 2 to derive a relationship between local measurements of V, P and Q at each generator
node and remote system conditions, either |
Iij|
or Qij for each branch of the network where thermal constraints are of concern. In
WO2015/193199 the three expressions employed to minimise the flow of reactive power on remote lines
are formulated as follows:
- the flow of line current or reactive power (reactive power including a current component)
on a remote line of concern to a local generator:
- the local voltage magnitude at minimum system demand:
- the local reactive power voltage sensitivity:
[0021] It will be seen that each of equations (8) to (10) comprises a second order expression
relating two local measurements from V, P and Q at the generator node, to a parameter
on the network calculated from the power flow analysis, e.g. line current |
Iij| or reactive power flow
Qij on a branch of the power system. However other orders can be used and a greater number
of independent variables, for example, measured values for adjacent generators, could
also be chosen to extend this technique. Equally, the expressions need not be continuously
valued functions and could possibly be non-linear.
[0022] It should be noted that any generator of the network could be concerned with its
impact on more than one branch and in this case, a plurality of vectors x, each associated
with a respective branch ij, would be calculated for that generator.
[0023] It should also be appreciated that if the status of the line was communicated to
the controller this would improve the estimate for branch flow calculated from equation
(8).
[0024] The vectors x, y and z in equations (8) to (10) are the coefficients determined from
the regression analysis. The local measurements that are used for equation (8) are
the active power, P, and the voltage magnitude, V, obtained from measurements at the
location of the generator, equation (8). To calculate the voltage at a generator node
at minimum system demand, the active power, P, and reactive power, Q, measurements
are used in equation (9). Lastly to infer the local reactive power voltage sensitivity,
the measured local voltage, V, and reactive power, Q, of the generator are used in
equation (10).
[0025] Formulated this way, equations (8) to (10) provide for an indirect method of determining
the optimal solution to the reactive power management problem for distribution systems
with distributed generation, where the local voltage magnitude V and active and reactive
power generation P and Q, measured in real-time, are used to infer system conditions.
[0026] Figure 3 shows a typical illustration of two conflicting constraints for a given
generator; the local voltage at a generator node
i and line current of a remote branch of the system affected by the generator, as the
active and reactive power of the unit vary. Thus, in the example, of Figure 1, the
controlled node could be node 08 to which generator D is connected and the branch
of concern may be the branch connecting nodes 01 and 07 and/or nodes 07 and 08. (This
choice of branch of concern is typically not arbitrary, as for example, generator
D would not be regarded as affecting line current in the branch connecting nodes 02
and 03.)
[0027] Other assignments within the network of Figure 1 include the branch connecting nodes
01 and 02 being the branch of concern for generator A, branch 01-05 being the branch
of concern for generator B, and branch 01-07 being the branch of concern for generator
C.
[0028] Figure 3 highlights the available choice of reactive power in the solution space
available to the generator, if the active power is assumed fixed, as indicated by
the constant active power curve. The typical protocol for a system operator if a thermal
constraint breach is detected on a line such as this, i.e. if the maximum line current
for the remote branch of concern is (to be) exceeded, has been to request a reduction
in the active power produced by the generator. This curtailment of power generation
has the effect of moving the constant active power curve in the direction of the arrow
C. However, as will be seen, this can involve a substantial curtailment of power generation
to bring the generator operator point to a level where maximum line current is not
exceeded.
[0029] FIG. 4 shows the sequential steps involved in the control of a generator as disclosed
in
WO2015/193199. Step 1 comprises the modelling and regression analysis described in relation to
Figure 2.
[0030] In Step 2 of FIG. 4, a controller (not shown) with access to equations (8) to (10)
described above takes local measurements at a generator's terminals; voltage magnitude,
V, active power, P, and reactive power generation, Q. Measurement can comprise continuous
or periodic monitoring, for example, at 15 minute intervals, or indeed can be event
driven for example in response to changes in demand or active power generation.
[0031] The controller can either comprise a centrally located controller in communication
with each generator and provided with the equations for each generator; or alternatively
independent controllers could operate at each generator with only knowledge of the
equations (8) to (10) for that generator.
[0032] Based on the local measurements of V, P and Q, in Step 3, the controller calculates
a target voltage
which will result in the minimal flow of current |
Iij| or reactive power flow
Qij on a branch ij in the surrounding network.
[0033] An optimal solution is obtained by first determining a local voltage set point,
from Equation (8) that results in the minimal current flow |
Iij| and, by extension, the calculated negation of reactive power flow, in the target
branch. Taking equation (8), which describes the current flow of a branch on the power
system, the minimum is found by substituting the observed value of the measured independent
variables, e.g. active power generation P, and differentiating with respect to the
control variable, e.g. the voltage, V, at the terminals of the generator. The resulting
expression of the gradient is set to zero and solved for the unknown control variable.
Graphically, this corresponds to locating
shown in Figure 3.
[0034] As an alternative, equation (8) can be used to equate to the reactive power flow
Qij of a remote line. In this case, as the values are not absolute, the roots of the
equation are found by substituting for the measured independent variables, e.g. active
power P. The value of this root will reveal the set point of the independent control
variable, e.g. voltage magnitude,
which will in turn result in the predicted nullification of reactive power flow on
a branch
ij on the power system.
[0035] Recall that these methods rely on the assumption that the offline power flow analysis
is undertaken for minimum system demand and, as such, the determined optimal set point
for the voltage,
is only optimal in the case of minimum demand. This simplification needs to be addressed
as, in reality, system demand varies daily and seasonally on a power system.
[0036] FIG. 5 illustrates the process of adjusting
calculated based on equation (8) to determine the target voltage
while accounting for the increase in system demand.
[0037] As shown, using the voltage set point,
outside the time of minimum demand, where the measured voltage at the node is
VObs, would require the injection of more reactive power than is necessary at minimum
demand where the voltage, as calculated from Equation (9), is modelled as
VMinD. This is due to the fact that the measured voltage magnitude,
V =
VObs, is lessened due to the increased active and reactive load drawn at above minimum
demand. The adjustment of the target voltage
calculated using equation (8) is given by:
where
VMinD is the solution to Equation (9), the calculated voltage based on observed P and Q
measurements of the generator at minimum system demand.
[0038] Thus, as shown in Figure 5, the required change in voltage, Δ
VNew, is found from the difference between the observed voltage
V =
VObs, from Step 2, and the adjusted target voltage set point
[0039] The controller checks that the target voltage set point
required by the optimal solution is within the bounds permitted by the system operator,
as in Equation (12):
[0040] In the event that the addition of Δ
VNew exceeds the imposed bounds, the required change in voltage is adjusted (typically
reduced) by the necessary amount to ensure that the limits V
-, V
+ are adhered to.
[0041] It will be appreciated that where equation (9) is modelled based on a given demand
other than minimum demand e.g. maximum demand, then the adjustment of
to arrive at
would need to be altered accordingly.
[0042] Step 4 of FIG. 4 calculates the up-to-date reactive power voltage sensitivity
of the location of the generator. This local sensitivity is calculated by substituting
the measured values V, Q from Step 2 of Figure 4 into Equation (10).
[0043] Step 5 of FIG. 4, determines the change in reactive power generation required by
the generator to obtain the required voltage set point
at its terminals. Using the sensitivity
found from Step 4, the required change in voltage Δ
VNew determined from Step 3, equation (13) is used to get the required change in reactive
power Δ
Q needed at the measurement location (the generator node):
[0044] To ensure the required change in reactive power is contained to the reactive power
limitations of the generator, the following inequality constraint is adhered to:
[0045] The upper and lower bounds of equation (14), can also be set to a limit imposed by
the system operator if a power factor or PQ range is required. In the event of the
change in reactive power breaching the bounds Q
- and Q
+, the change in reactive power is adjusted to bring the realised reactive power output
to that bound. This control instruction is issued to the existing generator control
system, Step 6.
[0046] It should also be appreciated that where a generator is concerned with conditions
at more than one network branch, Steps 3-5 of Figure 4 can be repeated based on each
vector x (used in equation (8)) to provide alternative solutions for the required
change in reactive power Δ
Q. The largest calculated permitted change in reactive power can be chosen.
[0047] Should the generator be operating at its reactive power limit or voltage limit and
the operating conditions suggest that the assigned branch (or branches) is in breach
of its thermal limit, an instruction to reduce the active power generation by the
required amount could be given. Formulating the active power voltage sensitivities
to the local measurement set would then be useful.
[0048] On adjusting the reactive power of the generator by the required amount Δ
Q, the procedure from Step 2 - Step 5 is then repeated and can be followed indefinitely.
[0049] A summary of the procedure to obtain the control signal Δ
Q from these local inputs V, P and Q is provided in FIG. 6. So, in step 60, equation
(8) is used to calculated an optimal voltage
In step 62, equation (9) is used to calculate the expected voltage at a generator
node at minimum demand
VMinD. In step 64, these values are combined with the observed voltage at the node
V =
VObs and checked against system bounds to produce a required voltage change Δ
VNew as illustrated in Figure 5. Separately and either in parallel or sequentially, equation
(10) is used to determine voltage sensitivity
at the node. In step 68, these values are combined and checked against system bounds
to provide a required change in reactive power Δ
Q and this is communicated by the controller to the generator.
[0050] Referring back to Figure 3, the command to change reactive power has the effect of
nominally shifting a generator operating point along the constant active power curve
and as will be appreciated, this can provide a solution which enables a generator
to adhere to thermal constraints without necessarily curtailing its active power generation.
So for example, the method could help to find the operating point T, rather than shifting
the operating point in the direction of the arrow C.
[0051] WO2015/193199 discloses using local measurements (P,V,Q) at the point of connection of a generator
(A,B,C,D) to a network to infer remote system conditions (
Qij) and calculate an optimal mode of operation to maximise energy export and reduce
energy losses on a remote section of network. This ensures the local voltage limit
(V
+) at the point of connection of the generator is adhered to while simultaneously minimising
current flow of the connected network without requiring any form of communication
between generators. Less current will flow, which ultimately reduces the energy losses
on network branches while actually maximising the active power generated by providers.
This ensures the minimal flow of reactive power is present on the surrounding branches
connecting a generator node to the network and fully utilises the reactive power of
a generator to accommodate the voltage rise effect from active power generation.
[0052] However, as indicated above, operating conditions may still be such that adjustment
of reactive power alone may not be sufficient to enable a generator to adhere to thermal
constraints and so some reduction in active power generation may be required.
[0053] Whereas
WO2015/193199 looked to minimise the flow of reactive power on a remote branch, an embodiment of
the present invention described in more detail below maximises the flow of active
power on a remote branch of the distribution system and, as a by-product, the reactive
power flow is minimised.
[0054] Similar to methodology disclosed in disclosed in
WO2015/193199, upholding the remote thermal limit and local voltage limit should be done by first
calling upon the reactive power of the generator. Active power curtailment should
be the last resort of a controller.
[0055] Referring to Figure 7 where the cells designated X identify the scenarios where curtailing
active power is needed to some degree:
Scenario 1: A generator at its (inductive) reactive power limit, the voltage limit is breached
and there is excess flow on the remote branch.
Scenario 2: A generator with voltage limit breach and excess flow on remote branch has been estimated,
reactive power resource available to potentially alleviate both.
Scenario 3: A generator at its (inductive) reactive power limit and the voltage limit is breached,
no complex power flow breach estimated, active power needed to be curtailed.
Scenario 4: A generator at an upper voltage limit, no flow constraint breach estimated, reactive
power available to potentially alleviate voltage constraint. Scenario 5: A generator at its (inductive) reactive power limit and the complex power rating
of a branch is estimated to be reached; curtailment of active power is needed.
Scenario 6: A remote branch has reached its complex power rating despite no voltage limit breach
and the limits of reactive power are not met. Curtailment of active power is needed.
Scenario 7: A generator at its (inductive) reactive power limit, no constraint breach in play.
Scenario 8: No constraint breach detected and reactive power resource available.
[0056] Failure to mitigate the breaches in Scenario 2 or 4 by using the reactive power resources
may lead to the curtailment of active power. In Scenario 7 with a further increase
in active power generation, the local voltage limit or the complex flow limit could
be breached. Only in Scenario 1, 3, 5 or 6 should active power curtailment be considered.
[0057] The present embodiment identifies which of the above scenarios is to be solved so
as all constraint conditions can be satisfied.
[0058] In the present embodiment, one further formulation is required via-a-vis the approach
of
WO2015/193199; this is a formulation for the active power flow (
Pij) on a remote branch, modelled as per equation (15) below as a function of values
P and V and coefficients
w1...w6:
[0059] The coefficients
w1...w6 for
Pij can be calculated in the same fashion as the coefficients x and
y for
Qij and
VMinD respectively in
WO2015/193199 at minimum system demand. The equations for
Qij and
VMinD take the form described in
WO2015/193199.
[0060] Again, other orders of equations can be used for
Pij,
Qij and
VMinD and a greater number of independent variables, for example, measured values for adjacent
generators, could also be chosen to extend this technique. Equally, the expressions
need not be continuously valued functions and could possibly be non-linear.
[0061] Thus the methodology of
WO2015/193199 in formulating a current flow and reactive power flow estimation is adapted to determine
the relationship between the new estimated parameter, active power flow, as it relates
to the local active power injections of a generator and the local node voltage measurement
at the generator at minimum system demand.
[0062] As before, to obtain tolerable estimations i.e. a sum of square of residuals and
a root mean squared error of practically zero, a two-variable second order equation
is fitted to data acquired at minimum system demand.
[0063] The new estimate for active power flow on a remote branch,
Pij, together with the estimation of reactive power flow of the remote branch,
Qij, are used to estimate the complex power flow through the branch,
Sij, using the equation of complex power flow (16):
[0064] Referring to Figure 8, in the present embodiment, an optimal voltage set point is
found by differentiating the formulation of active power flow,
Pij, with respect to voltage and setting this equation to zero. This estimates the point
at which the formulation for the slope of the equation is zero, obtaining the optimal
voltage set point to maximise the flow of active power. This set point, in maximising
active power output of the remote branch will inherently result in the minimisation
of reactive power along the branch in question.
[0065] First, the active power set point is assumed to be fixed initially. This allows
Pij in equation (15) to be differentiated so as an optimal voltage for the node can be
determined as in equation (17):
[0066] As in Figure 5 of
WO2015/193199, the formulation of voltage at minimum demand,
VMinD, is checked to adjust the optimal voltage set point
and account for the presence of demand on the remote branch to provide
[0067] At this stage, the optimal voltage set point
is assessed as to whether or not it falls within the allowable range of the local
voltage constraint bounds. If not, the approach proceeds with the upper voltage limit
V
+ as the target voltage set point rather than
knowing that this will cause a flow of reactive power on the remote branch and also
cause an increase in active power loss.
[0068] Where there is a voltage constraint breach, a sub-optimal solution will be obtained
for the maximum possible flow along the remote branch. The voltage constraint requires
the use of local reactive power resources to manage the voltage at the upper limit
V
+, (Scenarios 1 - 4).
[0069] In any case, the remote complex power flow is estimated for this voltage set point
(
or V
+) and current active power set point P, as in equation (18) to determine if a complex
power constraint
for the branch could be breached:
[0070] This check can go one of two ways, either there is a complex flow constraint breach
or not. In scenarios 1 -4, if there is a constraint breach at this stage, it means
that the flow of reactive power needed to support the local voltage constraint has
caused the upper limit breach. This flow of reactive power is required and, as such,
where there is a constraint breach (scenarios 1 and 2), the active power flow needs
to be curtailed with a change (Δ
P). Or rather the combination of active power and reactive power of the generator should
simultaneously manage both the flow of complex power and the voltage constraint, but
priority needs to be given to the active power. The sub optimal solution still exists
at the upper voltage limit, and the upper complex power bound is a known parameter
V
+, which means in the formulation of (18), the only unknown is the active power generation
that will satisfy this condition. Expanding (18), by squaring out both sides, setting
the equation to equal the known complex power limit
and further expanding the square of the two quadratic formulations of active power,
Pij, and reactive power flow,
Qij, gives the quartic expression (19):
[0071] Combining the coefficients gives a simplified expression for
P, the required active power generation setting (20).
[0072] Solving for the roots of this equation gives three impossible answers (negative power
or complex expressions) and one attainable/possible answer for the required active
power setting for the node.
[0074] The next calculation determines the new value for Q, the required reactive power
setting of the generator that, together with the new value for P, will result in the
voltage set point, in this case
at the node of generation. For this calculation, the coefficients
y1...y6 and formulation for local voltage (
VMinD.) as a function of active power and reactive power of the generator is used. This
results in a quadratic expression in
Q, with a known value of active power (P) and target voltage set point
[0075] Solving for the roots of this equation gives the required new reactive power set
point, Q, for the generator.
[0076] If, after assessing that there would be a voltage constraint and by checking (18)
there is no constraint breach on the remote flow (Scenarios 3 - 4), then the procedure
jumps straight to (21) where a change in value for reactive power Δ
Q is determined.
[0077] In the event that there was no voltage constraint breach after determining the optimal
voltage
(Scenarios 5 - 8) the procedure again checks for a complex flow constraint breach.
Assuming there is no complex flow breach (Scenarios 7 - 8), then the change in value
for reactive power Δ
Q can again be found from (21).
[0078] If however, with no voltage constraint breach (Scenarios 5 - 6), the condition of
(18) cannot be satisfied then active power is required to be curtailed by Δ
P. Note that the voltage set point
obtained from equations (15, 17) is only optimal for the case where the active power
generation is assumed fixed and does not to cause a breach in remote complex power
flow. When this is not the case, equation (15) now has two unknown variables
P and
V and so in isolation cannot be used to determine both optimal set points. Another
equation is required to condition the problem as two equations and two unknowns. Given
that the sole constraint is the complex power flow along a remote branch, the solution
ensures that only active power is flowing on this branch. Therefore the complex flow
limit
can be equated to active power flow (
Pij) as per equation (22). Also the reactive power in this branch, as always, should
be ideally zero to maximise the active power export as per equation (23):
[0079] This gives the two conditions (22, 23) required to satisfy the complex flow constraint.
These estimations for active power and reactive power flow can be simplified as first
order equations, as per equations (24) and (25). This requires the procedure of Figure
2 to be repeated to relate a first order equation with two independent variables to
the flow of active power and reactive power on a remote line.
[0080] Solving for the voltage and active power in this set of equations gives the amount
of voltage curtailment (Δ
V) needed to satisfy the flow constraint on the remote branch. We can then use the
curtailed voltage set point as the known parameter in equations (19, 20) to determine
a curtailment in active power (Δ
P) and then with these new values for voltage and active power, determine a new setting
for reactive power using equation (21). In doing so, the optimal reactive power and
voltage set point with minimal curtailment to the active power generation is found.
[0081] Again, in Figure 8, the labels denoted S. 1 - 8 relate the regions in the flow chart
back to the scenarios identified in Figure 7 and discussed above. In the present embodiment,
a target voltage
for the maximisation of active power flow is calculated. The target voltage is checked
to be within tolerable bounds and that the resultant estimate for complex power flow
S
ij is within tolerable bounds. In some scenarios, a curtailment in active power Δ
P to obtain this optimal voltage may be required. Finally, an alternate approach vis-à-vis
the first embodiment is used to calculate a required change in reactive power Δ
Q.
[0082] When operating within the thermal limit of the target branch,
WO2015/193199 and the present embodiment produce the same results.
WO2015/193199 minimises the content of reactive power in a target branch, while the present embodiment
maximises the content of active power in a target branch which results in minimal
reactive power flow. However the formulation of active power flow gives rise to the
estimation of complex power flow, using the estimates for active power flow and reactive
power flow. All branches have a known complex power limit, thus the present embodiment
identifies if that upper limit is breached and calculates the changes in reactive
power as well as active power required to avoid such a breach.
[0083] The estimation for current flow on a remote branch in
WO2015/193199 is imprecise, typically giving an error of about 5%. In contrast, this new estimate
for complex power flow employed in the present embodiment, being conservative, gives
an error of about 0.05%.
[0084] The controller can be implemented in software for example on a programmable logic
controller (PLC) device with limited computational ability which is installed at a
generator substation and interfaced to an existing generator control system. Prior
to operation, the remote section/s of network which is/are to be assigned to the generator
is/are established and the controller is programmed with vectors x, y and w from equations
(15), (17), (19) and (21)-(23) for the present embodiment. The input measurement set
is obtained from local instrument transformers, which are readily available at the
generator substation. Once in operation, the controller issues an updated reactive
power set point and, possibly in the present embodiment, an active power set point
for the generator at given intervals. It is of course possible for the controller
to update more or less frequently or at irregular intervals.
[0085] In
WO2015/193199, in the event that a change in the topology of the network occurs, and/or a generator
or load is added to or removed from the network, the network needs to be remodelled,
i.e. Figure 2 or step 1 of Figure 4 need to be repeated, and the controller software
updated accordingly to take account the new network characteristics. In embodiments
of the present invention, the controller could be arranged to sense a change in topology
and to react accordingly.
[0086] The advantage in operating distributed generators in the manner described above is
the assurance that the generated active power is added as efficiently as possible
to the surrounding network. This is typically reflected in an improvement in system
losses over the course of operation of the method.
[0087] This invention is applicable to any generator, especially a renewable power generator
that has the ability to control the injection and absorption of reactive power. Embodiments
of the invention can be applied to regions of a transmission system with high reactive
power voltage sensitivities and reactive power voltage angle sensitivities.